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This paper addresses the identification of patterns and change points in high-dimensional time series data recorded by body-attached sensor networks. We discuss different approaches to state detection in a supervised and in an unsupervised learning scenario. Within this context, we provide empirical evidence that our methods are capable of identifying relevant motion patterns in complex time series. Among other conceivable applications, we are particularly motivated by the idea that recognized motion change points serve as valuable information for embodied digital technologies interacting in public spaces.

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A Computational Study on Supervised and Unsupervised Gait Data Segmentation

  • 章节

  • 出版模式：

    开放获取

  • 发表日期：

    2026年

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  • Christoph Helmberg,
  • Tobias Hofmann,
  • Dominik Krumm,
  • Stephan Odenwald

摘要

This paper addresses the identification of patterns and change points in high-dimensional time series data recorded by body-attached sensor networks. We discuss different approaches to state detection in a supervised and in an unsupervised learning scenario. Within this context, we provide empirical evidence that our methods are capable of identifying relevant motion patterns in complex time series. Among other conceivable applications, we are particularly motivated by the idea that recognized motion change points serve as valuable information for embodied digital technologies interacting in public spaces.